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Revised intensity frequency-duration (ifd) design rainfalls estimates for wa - janice green

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Revised intensity frequency-duration (ifd) design rainfalls estimates for wa - janice green

  1. 1. Revised Intensity-Frequency-Duration (IFD) Design Rainfalls Estimates for WA Janice Green Bureau of Meteorology 24 October 2012
  2. 2. IFD Revision Team• Team members – Cathy Beesley *Chris Lee – Fiona Johnson *Maria Levtova – Catherine Jolly *William Tall – Garry Moore *Max Monahan – Cynthia The *Damian Chong – Karin Xuereb *Murray Henderson *Ceredwyn Ealanta – Mike Hutchinson (ANU) (Honorary team member) – University of Western Sydney – Student contractors
  3. 3. Current IFDs – AR&R87
  4. 4. Current IFDs – AR&R87
  5. 5. Current IFDs – CDIRS On-line
  6. 6. Current IFDs – CDIRS On-line
  7. 7. Current IFDs – CDIRS On-line
  8. 8. Current IFDs – CDIRS On-line
  9. 9. Current IFDs• Developed by Bureau of Meteorology over 20 years ago• Used a database comprising information primarily from the Bureau’s network of daily read and pluviograph stations• Adopted statistical techniques considered appropriate at the time• Focus of the IFDs was the design of structures on relatively large rural catchments and therefore durations of less than five minutes were not considered necessary.
  10. 10. Current IFDs – Adopted Approach Aspect ARR87Data BoM stations onlyRecord length ~ up to 1983; 7500 daily read > 30 years; 600 pluviographs > 6 yearsFrequency analysis Annual maximum series; method of moments; Log-Pearson Type IIIDaily to sub-daily Principal Component AnalysisMapping Subjective (meteorological analysis)Frequency ARIs 1 year to 100 yearDuration 5 minute to 72 hour (3 day)Dissemination Maps; HAS; CDIRS on-lineClimate change Stationary climate assumed; climatic trends negligible effect on IFDs
  11. 11. Basic approachRainfall data  Establishment of data base – Bureau and Water Regs  Quality controlling of data Series of  Annual Maximum Seriesextreme values  Choice of fitting technique and distribution Frequency  Extraction of L-moments Analysis  Supplementing of sub-daily statisticsRegionalisation  Index rainfall approach  Regions of Influence Gridding  Choice of variables  Gridding technique  OutputsDissemination  Medium
  12. 12. Data base• Bureau of Meteorology Australian Data Archive for Meteorology (ADAM ) – Contains 19711 daily read rainfall stations (both open & closed) for period from 1800 to 2011 – Contain 1467 continuous stations – both pluviograph & TBRG• Water Act 2007 identified the Bureau’s new responsibilities including collecting and publishing water information• Water Regulations 2008 provided ready access to rainfall data collected by other organisations• In particular, data from dense continuous rainfall networks operated by urban water utilities and councils: – ~350 daily read rainfall stations – ~2175 continuous rainfall stations
  13. 13. Spatial coverage of daily read rainfall stations
  14. 14. Spatial coverage of continuous rainfall stations•
  15. 15. Data base• Finalisation of data base – Initially December 2010
  16. 16. Data base• Finalisation of data base – Initially December 2010
  17. 17. Data base• Finalisation of data base – Initially December 2010 – Updated to December 2011
  18. 18. Data base• Finalisation of data base – Initially December 2010 – Updated to December 2011
  19. 19. Data base• Finalisation of data base – Initially December 2010 – Updated to December 2011 – Updated to March 2012 – ?????
  20. 20. Quality Control• Previous work had undertaken QCing on a largely manual basis• Enormous amount of data that needed to be quality controlled – > 20 000 daily read stations - Bureau – > 1500 pluviograph stations – Bureau – ~350 daily read rainfall stations – Water Regulations – ~2175 continuous rainfall stations – Water Regulations• Disparate amount and type of QCing undertaken by data providers• Necessitated automating as much of QCing as possible• However still required manual QCing using Bureau’s Quality Monitoring System (QMS)
  21. 21. QCing daily read data• Quality Controlling of daily read data: – Infilling of missing data – Disaggregation of flagged accumulated daily rainfall totals – Detection of suspect data, identification and correction of: • Unflagged accumulated totals • Time shifts – Identification of gross errors - data inconsistent with neighbouring records but not either of the above two categories – Manual correction gross errors identified as having a high probability of being incorrect
  22. 22. QCing daily read data using QMS
  23. 23. QCing of continuous data• QCing of continuous rainfall data considerably more complicated: – significantly more data due to shorter time step – sparsity of continuous station network means fewer stations with which to compare – small rainfall depths extremely difficult to QC• Needed to reduce amount of data to be QC’d to a manageable amount• Developed approach QC’d the PDS – 5 x number of years of record• Durations of: – 5, 10, 15, 30 minutes – 1, 2, 3, 6, 12 hours – 1, 2, 3 days• Number of PDS values to be QC’d > 1,000,000
  24. 24. QCing continuous data• Issues with Continuous Rainfall Data – Time shifts of clock - DINES – Missed pulses – TBRG• QCing procedure compared values to: – AWAP (Australian Water Availability Product) gridded daily rainfall data – Co-located or nearby daily read stations – AWS (Automatic Weather Stations) – Synoptic stations
  25. 25. Trialling of frequency distributions• Frequency analysis – Previously adopted Log-Pearson Type III fitted by method of moments – Used 58 Bureau long-term continuous rainfall stations to trial a range of distributions• From each of the 58 continuous rainfall stations extracted both the AMS and the PDS
  26. 26. Trialling of frequency distributions• AMS & PDS extracted for durations of : – 6, 12, 18, and 30 minutes – 1, 2, 3, 6, and 12 hours• Calculated L-moments and fit five distributions: – Generalised Logistic (GLO) – Generalised Extreme Value (GEV) – Generalised Normal (GNO) – Pearson Type III (PE3) – Generalised Pareto (GPA).• Assessed goodness of fit using Hosking and Wallis (1997) goodness of fit measure ZDist
  27. 27. Trialling of frequency distributions• AMS – the GEV gave the most acceptable fit for all durations except 3 and 12 hours – however, with the exception of the GPA, the other distributions also showed acceptable fits• PDS – the GPA gave the most acceptable fit for all durations
  28. 28. Extraction of L-moments• L-moments used to summarise statistical properties of AMS and PDS – Index rainfall (mean) – L-skewness – L-CV• L-moments expected to be more robust against large outliers in the data, particularly for the higher order moments.• To reduce uncertainty in the parameter estimates, minimum station record lengths have been adopted – 30 years for daily rainfall stations and – 9 years for sub daily rainfall stations.
  29. 29. Estimation of sub-daily rainfalls• Shift in focus to urban design on small catchments necessitating the provision of IFD estimates for durations as short as one minute• Far fewer continuous rainfall stations than daily read rainfall stations
  30. 30. Spatial coverage of daily read rainfall stations
  31. 31. Spatial coverage of continuous rainfall stations•
  32. 32. Derivation of short duration IFDs• Need a method to improve spatial coverage of sub-daily data• Most commonly done using information from daily stations – Statistics of sub-daily data are inferred from those of daily data• Techniques adopted include: – Factoring of the 24 hour IFDs – Principal component analysis (PCA) – Partial least squares regression (PLSR)
  33. 33. Derivation of short duration IFDs• However, major weakness of the previously adopted approaches is their inability to account for: – Variation in record lengths from site to site – Inter-station correlation• An approach that avoids these problems is Bayesian Generalised Least Squares Regression (BGLSR)
  34. 34. Approach to be adopted• Statistics to be derived (predictands) are: – Index rainfall (mean) – L-skewness – L-CV• Predictors to be used are: – Location (latitude & longitude) – Elevation – Slope – Aspect – Distance from coast – Mean annual rainfall – Index rainfall, L-skewness & L-CV at 24, 48 & 72 hours
  35. 35. Regionalisation• Regionalisation recognises for stations with short records – considerable uncertainty when estimating the parameters of probability distributions and – short records can bias estimates of rainfall statistics• Overcome by combining information from multiple rainfall stations – more accurate estimates of the probability distribution parameters can be made
  36. 36. Regionalisation• Index rainfall approach adopted to do this (Hosking & Wallis)• Station point estimates have been regionalised using a Region of Influence Approach (ROI).• Trialled various approaches => ROIs defined as circle which is expanded until it includes 500 station years of record• Circular ROIs defined with distance defined in three dimensions – Latitude – Longitude – Elevation
  37. 37. Region of influence
  38. 38. Region of influence
  39. 39. Region of influence
  40. 40. Gridding• Regionalisation gave estimates of GEV parameters at all station locations – Combined with the mean of the AMS (index) at that site to estimate rainfall quantiles for any required exceedance probability.• However IFD estimates required across Australia, not just at station locations.• Results of the analyses needed to be extended in some way to ungauged locations.
  41. 41. Gridding• Translation from point to gridded rainfall estimates carried out with thin plate smoothing splines implemented using ANUSPLIN.• ANUSPLIN (Hutchinson 2007) was chosen to grid the GEV parameters so that IFD estimates are available for any point in Australia.• GEV parameters are being gridded in ANUSPLIN, as: – earlier testing showed little difference in quantile estimates if point parameter or point rainfall depths gridded. – Gridding rainfall parameters gives more flexibility in the choice of exceedance probabilities that can be extracted and – requires fewer grids to be processed in ANUSPLIN.• Appropriate level of smoothing chosen through generalised cross validation by minimising the predictive error of the fitted surface
  42. 42. Interpolation – point to grid SHAPE GRID SCALE GRID MEAN GRID
  43. 43. GriddingIndex Alpha Kappa
  44. 44. Interpolation – point to grid Y = 1 in 100 AEP Y = 1 in 50 AEP Y = 1 in 20 AEPSHAPE GRID Y = 1 in 10 AEP Y = 1 in 5AEP Y = 1 in 2 AEP SCALE GRID MEAN GRID
  45. 45. ANUSPLIN Output Example
  46. 46. Outputs• Revised IFDs will be provided as depths in millimetres (not intensities)• Revised IFDs will be provided for standard durations of: – 1, 2, 3, 4, 5, 10, 15, 30 minutes – 1, 2, 3, 6, 12 hours – 1, 2, 3, 4, 5, 6, 7 days – advice provided for IFDs < 1 minute and > 7 days
  47. 47. Outputs• Revised IFDs will be provided for standard EY and AEPs of : – 1EY (1 Exceedance Per Year) } New AR&R probability – 50%, 20%, 10%, 5%, 2%, 1% AEP } terminology• Revised IFDs also provided for sub-annual recurrences eg 2EY• Revised IFDs blended with CRCFORGE estimate to enable smooth curve to be derived to an AEP of 0.05%
  48. 48. Revised IFDs• Revised IFDs will be disseminated – In electronic form – Via new web page accessed from Bureau of Meteorology website• Release of new webpages in 2 Phases
  49. 49. Phase 1• Phase 1 – Revised IFDs for single point – For standard durations & EY/AEPs – Functionality of Phase 1 web pages => same as current CDIRS web page – CDIRS and IFD 2012 run in parallel for ~ 6 months
  50. 50. Protoype
  51. 51. Phase 2• Phase 2 – Multiple locations – Dynamic map – Duration range filters – Uncertainty limits – Temporal pattern – Areal reduction factors – Climate change adjustments – Non-standard durations & EYs & AEPs – Rainfall frequency curve to 0.05% AEP
  52. 52. Outputs• Climate Change – Revised IFDs will be for current climatic regime – AR&R Revision Climate Change Research Strategy has been developed – Objective to identify research priorities to enhance understanding of how projected climate change may alter the behaviours of factors used to estimate design floods
  53. 53. Outputs• Climate Change – Identified 5 research themes: • Rainfall intensity-frequency-duration relationships • Rainfall temporal patterns • Continuous rainfall sequences • Antecedent conditions (including baseflow) • Simultaneous extremes – Research to be undertaken over both short term (Stage 1 – one year) and longer term (Stage 2 – four years) – Stage 1 of first two themes funded by GA
  54. 54. More information….Leave your business cardJanice Green(02) 6232 3558j.green@bom.gov.auorifdrevision@bom.gov.au
  55. 55. Protoype
  56. 56. Protoype
  57. 57. Protoype

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